Evaluating the performance of land surface model ORCHIDEE-CAN v1.0 on water and energy flux estimation with a single- and multi-layer energy budget scheme

Geosci. Model Dev., 9, 2951–2972, 2016 www.geosci-model-dev.net/9/2951/2016/ doi:10.5194/gmd-9-2951-2016 © Author(s) 2016. CC Attribution 3.0 License. Evaluating the performance of land surface model ORCHIDEE-CAN v1.0 on water and energy flux estimation with a singleand multi-layer energy budget scheme Yiying Chen1,a, James Ryder1, Vladislav Bastrikov1, Matthew J. McGrath1, Kim Naudts1,b, Juliane Otto1,c, Catherine Ottlé1, Philippe Peylin1, Jan Polcher2, Aude Valade3, Andrew Black4, Jan A. Elbers5, Eddy Moors5, Thomas Foken6, Eva van Gorsel7, Vanessa Haverd7, Bernard Heinesch8, Frank Tiedemann9, Alexander Knohl9, Samuli Launiainen10, Denis Loustau11, Jérôme Ogée11, Timo Vessala12,13, and Sebastiaan Luyssaert1,d 1Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France 2Laboratoire de Météorologie Dynamique (LMD, CNRS), Ecole Polytechnique, Palaiseau, France 3Institut Pierre Simon Laplace, Place Jussieu 4, 75010 Paris, France 4Land and Food Systems, University of British Columbia, Vancouver, BC, Canada 5Alterra, Wageningen UR, Wageningen, the Netherlands 6Department of Micrometeorology University of Bayreuth, Bayreuth Center of Ecology and Environmental Research, Bayreuth, Germany 7CSIRO, Marine and Atmospheric Research, Canberra, Australia 8Dept. Biosystem Engineering (BIOSE), University of Liege, Gembloux, Belgium 9Dept. Bioclimatology, Georg-August University of Göttingen, Büsgenweg, Göttingen, Germany 10Natural Resources Institute Finland, Vantaa, Finland 11INRA UMR 1391 ISPA Centre de Bordeaux Aquitaine, Bordeaux, France 12Department of Physics, University of Helsinki, Helsinki, Finland 13Department of Forest Sciences, University of Helsinki, Helsinki, Finland anow at: Research Center for Environmental Changes (RCEC), Academia Sinica, Taipei, Taiwan bnow at: Department of Land in the Earth System, Max Planck Institute for Meteorology, Hamburg, Germany cnow at: Climate Service Center Germany (GERICS), Helmholtz-Zentrum Geesthacht, Hamburg, Germany dnow at: Department of Ecological Sciences, VU University, Amsterdam, the Netherlands Correspondence to: Yiying Chen ([email protected]) Received: 2 February 2016 – Published in Geosci. Model Dev. Discuss.: 25 February 2016 Revised: 5 August 2016 – Accepted: 8 August 2016 – Published: 2 September 2016 Abstract. Canopy structure is one of the most important vegetation characteristics for land–atmosphere interactions, as it determines the energy and scalar exchanges between the land surface and the overlying air mass. In this study we evaluated the performance of a newly developed multilayer energy budget in the ORCHIDEE-CAN v1.0 land surface model (Organising Carbon and Hydrology In Dynamic Ecosystems – CANopy), which simulates canopy structure and can be coupled to an atmospheric model using an implicit coupling procedure. We aim to provide a set of acceptable parameter values for a range of forest types. Top-canopy and sub-canopy flux observations from eight sites were collected in order to conduct this evaluation. The sites crossed climate zones from temperate to boreal and the vegetation types included deciduous, evergreen broad-leaved and evergreen needle-leaved forest with a maximum leaf area index (LAI; all-sided) ranging from 3.5 to 7.0. The parametrization approach proposed in this study was based on three selected physical processes – namely the diffusion, advection, and turbulent mixing within the canopy. Short-term sub-canopy Published by Copernicus Publications on behalf of the European Geosciences Union. 2952 Y. Chen et al.: Evaluating the performance of ORCHIDEE-CAN v1.0 observations and long-term surface fluxes were used to calibrate the parameters in the sub-canopy radiation, turbulence, and resistance modules with an automatic tuning process. The multi-layer model was found to capture the dynamics of sub-canopy turbulence, temperature, and energy fluxes. The performance of the new multi-layer model was further compared against the existing single-layer model. Although the multi-layer model simulation results showed few or no improvements to both the nighttime energy balance and energy partitioning during winter compared with a single-layer model simulation, the increased model complexity does provide a more detailed description of the canopy micrometeorology of various forest types. The multi-layer model links to potential future environmental and ecological studies such as the assessment of in-canopy species vulnerability to climate change, the climate effects of disturbance intensities and frequencies, and the consequences of biogenic volatile organic compound (BVOC) emissions from the terrestrial ecosystem. 1 Introduction Today’s Earth system models (ESMs) integrate ocean, ice sheet, atmosphere, and land surface in order to provide a powerful tool to simulate the Earth’s past, present, and future climates (Drobinski et al., 2012). In such a model, the land surface sub-model provides the surface fluxes to the atmospheric sub-model, affects the dynamics of the planetary boundary layer, and exerts a strong influence on the climate. The dynamics of the simulated surface fluxes rely on the land surface sub-model that, over the past 40 years, has evolved from a simple bucket model approach towards sophisticated soil–vegetation–atmosphere transfer (SVAT) schemes (Pitman, 2003; Stöckli and Vidale, 2005). Although present-day land surface models differ from each other in their formulation and details, their performances show similar deficiencies. For example, imposing the same land cover changes to seven land surface models resulted in diverging climate effects. Amongst other factors, this divergence was due to the parametrization of albedo and the representation of evapotranspiration for different land cover types (Pitman et al., 2009). Difficulties in reproducing fluxes of sensible and latent heat for a wide range of vegetation types have been ascribed to the so-called “bigleaf” approach (Bonan, 1996; Sellers et al., 1996; Dickinson et al., 1998; Jiménez et al., 2011) which treats the surface as a isothermal large leaf. Potentially, representing the vertical canopy structure in detail and simulating radiation partitioning and turbulent transport within the vegetation will result in an improved determination of sensible and latent heat flux estimates (Baldocchi and Wilson, 2001; Ogée et al., 2003; Bonan et al., 2014). For example, several multi-layer SVAT schemes have been proposed and validated with site-level observations (Ogée et al., 2003; Staudt et al., 2011; Haverd et al., 2012; Launiainen et al., 2015). These studies demonstrated that both top-canopy and within-canopy fluxes and micrometeorological profiles could be captured by means of a sophisticated parametrization scheme to describe the vegetation dynamics and the coupling between the atmosphere and the canopy. Because the standard version of ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems) makes use of a big-leaf approach (Ducoudré et al., 1993; Krinner et al., 2005), improved model capacity and performance were aimed for by implementation of a multi-layer energy budget scheme (Ryder et al., 2016) that was integrated with vertically discrete reflectivity, photosynthesis, stomatal resistance, and carbon allocation schemes. This new design resulted in a new version of ORCHIDEE named ORCHIDEECAN (ORCHIDEE-CANopy, revision 2290) (Naudts et al., 2015). Despite its code including a multi-layer energy budget scheme (Ryder et al., 2016), ORCHIDEE-CAN is currently applied using a single-layer energy budget, due to a lack of validated parameters for the multi-layer energy budget scheme. In Ryder et al. (2016), the model was developed and tested for a single site. In this study, we compiled a set of within-canopy and above-canopy measurements of energy, water, and CO2fluxes and used these data to parametrize and validate the new multi-layer energy budget scheme in the ORCHIDEE-CAN v1.0 (revision 2754) global-scale land surface model. The data set allowed us to test the model under diverse environmental conditions in order to demonstrate that the numerics can deal with the variation that can be found in global ecosystems. For this we granted ourselves the freedom to derive a separate parameter set for each site. Model performance of the new multi-layer parametrization was compared against the existing single-layer model. By doing so we learned about the strengths and weaknesses of the model and its parameters. In subsequent studies, we will have to derive a single parameter set for each plant functional type (PFT) and test how well the model reproduces global patterns in, for example, evapotranspiration. 2 Methodology 2.1 Multi-layer energy budget scheme The multi-layer energy budget scheme used in this study was developed for global land surface models (Ryder et al., 2016) and the calculations differ from the more common big-leaf energy budget scheme in three aspects. The new scheme calculates the following: (a) within-canopy longwave and shortwave radiation based on a vertical leaf area index (LAI; m2m−2) profile, (b) a within-canopy and belowcanopy wind profile based on the vertical LAI profile, and (c) the dependency of stomatal resistance and aerodynamic resistance based on the microclimatological conditions along Geosci. Model Dev., 9, 2951–2972, 2016 www.geosci-model-dev.net/9/2951/2016/ Y. Chen et al.: Evaluating the performance of ORCHIDEE-CAN v1.0 2953 Table 1. Symbolic notation used throughout the paper. Symbol Description Unit a1,a2,a3,a4,a5tuning coefficients for CDeff unitless a6factor ceiling of the slope unitless a7critical friction velocity in the middle point of the S-shape function unitless a8factor to constrain the S-shape function unitless a9threshold for vegetation cover unitless a10 linear weighting factor unitless Aassimilation rate µmolm−2s−1 CDeff effective drag coefficient unitless CSconcentration of CO2at leaf surface ppm CD,i vertically discretized estimate for canopy drag coefficient unitless Dh,air heat diffusivity of air cm2s−1 Dh,H2Oheat diffusivity of water vapour cm2s−1 dlcharacteristic leaf length m fPgap over-story gap probability from Pgap fraction m2m−2 Gveg logic variable to indicate the growth status of the vegetation unitless g0residual stomatal conductance if the irradiance approaches zero ms−1 hsrelative humidity at leaf surface % hccanopy height m kidiffusivity for level im2s−1 k∗ imodified diffusivity for level im2s−1 ksurf conductance for the surface–atmosphere interface ms−1 LAIileaf area index at level im2m−2 Nu Nusselt number unitless Pm,i momentum shielding factor unitless PAI plant area index m2m−2 Rcorrelation coefficient between the simulation and the observation unitless R0maximum correlation coefficient unitless Rb,i boundary layer resistance at level ifor heat sm−1 R0 b,i boundary layer resistance at level ifor water vapour sm−1 Rs,i stomatal resistance at level ism−1 Re Reynold’s number unitless SLA specific leaf area m2g−1 STTaylor skill score unitless Tweek weekly mean air temperature K Tgtemperature threshold for under-story phenology K TLLagrangian timescale s u∗friction velocity ms−1 uivelocity at level ims−1 Vcmax carboxylation capacity µmolm−2s−1 Wbr weighting parameter for boundary layer resistance unitless Wnf near-field weighting factor unitless Wsf weighting parameter for soil–atmosphere conductance unitless Wsr linear reduction parameter for stomatal resistance unitless β3fraction of potential plant transpiration realized unitless β4fraction of soil evaporation realized unitless µkinematic viscosity of air cm2s−1 ˆσfratio of the variance of the simulations over the variances of observations unitless σwstandard deviation in vertical velocity ms−1 the LAI profile. All symbols are explained in Table 1. In the following paragraphs these calculations are further described. a. The multi-layer energy budget scheme makes use of the longwave radiation transfer scheme proposed by Gao et al. (1989) and Gu et al. (1999). The scheme simulates longwave radiation transport, as well as scattering and absorption, along a vertically layered leaf area distribution. The simulated longwave radiation within a layer depends on the emitted longwave radiation by all of its www.geosci-model-dev.net/9/2951/2016/ Geosci. Model Dev., 9, 2951–2972, 2016 2954 Y. Chen et al.: Evaluating the performance of ORCHIDEE-CAN v1.0 neighbouring layers. The shortwave radiation transfer scheme, developed by Pinty et al. (2006), was applied to the albedo calculation. The scheme computes the absorption, transmission, and reflection of incoming radiation by vegetation canopies, which depends on the solar zenith angle, the type of illumination (direct or diffuse), the vegetation type, and the vegetation structure. This scheme considers shortwave radiation both from visible and near-infrared bands and was originally developed for single-layer canopies, but has since been extended for use with layered canopies (McGrath et al., 2016). b. The wind profile and the vertical eddy diffusivity (k; m2s−1) are calculated using the one-dimensional second-order closure model of Massman and Weil (1999), which makes use of the LAI profile of the stand. It calculates wind profile and vertical eddy diffusivity based on Lagrangian theory. c. The aerodynamic resistance (Rb; sm−1) is calculated based upon the leaf boundary-layer resistance, which is estimated according to Baldocchi (1988). The stomatal resistance (Rs; sm−1) is calculated using a Farquhar–von Caemmerer–Berry-type C3 (Farquhar et al., 1980) and Collatz-type C4 photosynthesis model (Collatz et al., 1992) which simultaneously solves carbon assimilation and stomatal conductance at the leaf level but excludes mesophyll conductance calculation. ORCHIDEE-CAN v1.0 uses an analytical approach as described by Yin and Struik (2009) to calculate layered stomatal resistances which depend on the ambient air temperature, humidity, within-canopy CO2concentration, vegetation-specific maximum carboxylation rate, and water supply from the roots to the stomata. Readers are referred to Ryder et al. (2016) for a comprehensive description of the multi-layer energy budget, its assumptions, mathematical details, and a proof of concept. Note that in ORCHIDEE-CAN v1.0 LAI is calculated from a prognostic leaf mass by making use of a vegetation-specific specific leaf area (SLA; m2g−1). The calculation of the vertical and horizontal distributions of the leaf mass, and thus the vegetation canopy, depends on plant phenology, intrastand competition, forest management, and allometric relationships, and is detailed in Naudts et al. (2015). 2.2 Observational data For this study forest sites were retained if the following data were available: (a) short but intensive campaigns taking flux and profile measurements within and/or below the tree canopy, and (b) multi-year monitoring of top-canopy fluxes. Through numerous regional projects such as CARBOEUROPE, AMERIFLUX, Fluxnet Canada, OZFLUX, ICOS, and NEON, and efforts such as FLUXNET (Baldocchi and Wilson, 2001), multiple year-long time series are now commonly available, especially for the temperate and boreal zones in Europe, Japan, Australia, and North America. Site selection was thus mostly limited by the availability of within-canopy and below-canopy measurements. Eight flux observation sites (Table 2) met the aforementioned criteria, and represented various climates from the Mediterranean to the boreal zone and different vegetation types including broad-leaved summer green, broad-leaved evergreen and needle-leaved evergreen. Data were thus missing from needle-leaved summer green vegetation such as larch (Larix sp.) and tropical vegetation, so it was not possible to cover all of the forest types that are considered in ORCHIDEE-CAN. The short intensive campaigns, taking measurements within-canopy and below-canopy, usually extended for periods ranging from several days to a few weeks (Period I; Table 3). During intensive campaigns, vertical profile measurements of wind speed, temperature, and atmospheric humidity were typically conducted. Such measurements were sometimes complemented with profile measurements of sensible and latent heat fluxes, as well as sub-canopy radiation measurements (Period II and III; Table 3). Furthermore, our parametrization and validation set-up required that topcanopy observations had to be available for periods exceeding 1 year (Period IV; Table 3). A typical long-term set-up measured sensible and latent heat fluxes, longwave and shortwave incoming radiation, wind speed, atmospheric temperature, and humidity. Parametrization and validation utilize the ORCHIDEECAN v1.0 model simulations, and so climate forcing data were required to drive the simulations. Site-level weather observation, i.e. shortwave incoming radiation, longwave incoming radiation, two-dimensional wind speed, precipitation, snow, near-surface air pressure, and specific humidity were reformatted and gap-filled using the method proposed by Vuichard and Papale (2015). Weather observations are an integral part of both intensive campaigns and multi-year topcanopy flux monitoring. Hence, within a measurement site, flux, profile, and weather data were usually available at the same temporal resolution and over the same time periods. Finally, the forcing files were completed with the observed vertical LAI profiles. However, the temporal resolution of LAI was much lower than the resolution of the meteorological variables. When the total LAI was measured at a higher time resolution than its vertical profile, the observed total LAI was vertically distributed according to the observed relative vertical LAI distribution. Model parametrization (Sect. 2.3) and model experiments that aimed at testing the performance of only the multi-layer energy budget (see EXP1 and EXP3 in Sect. 2.5) made use of the observed LAI profiles. For the remaining two model experiments (see EXP2 and EXP4 in Sect. 2.5), ORCHIDEE-CAN v1.0 calculated the vertical LAI profiles following the carbon allocation and carbon turnover schemes, as described in Naudts et al. (2015). Geosci. Model Dev., 9, 2951–2972, 2016 www.geosci-model-dev.net/9/2951/2016/ Y. Chen et al.: Evaluating the performance of ORCHIDEE-CAN v1.0 2955 Table 2. Stand structure and data availability of the experimental sites. The maximum observed leaf area (LAI; m2m−2) of the over-story and under-story LAI (all-sided) are reported separately. The height of the over-story is expressed in metres. Udenotes wind speed, Tadenotes atmospheric temperature, and qadenotes atmospheric humidity. LE, H, and Rndenote the latent heat flux, the sensible heat flux, and the net radiation, respectively. +indicates that profile measurements were available. −indicates that no profile measurements were available. Site Code FI-Hyy FR-LBr NL-Loo DE-Bay CA-Oas AU-Tum DE-Hai BE-Vie Species Pinus sylvestris Pinus pinaster Pinus sylvestris Picea abies Populus sp. Eucalyptus sp. Fagus sylvatica Fagus sylvaticaa Leaf type Needleleaved Needleleaved Needleleaved Needleleaved Broadleaved Broadleaved Broadleaved Broadleaved Growth form Evergreen Evergreen Evergreen Evergreen Deciduous Evergreen Deciduous Mixed ORCHIDEE PFT 18 5 6 7 20 15 13 13 Over-story LAI 6.5 2.0 1.9 4.8 2.9 2.5 5.8 5.1 Under-story LAI 0.5 1.5 1.5 0.5 2.8 1.0 0.1 0.1 Height 17.0 23.0 15.0 15.0 22.0 50.0 30.0 25.0 Uprofile + − + + + + + + Taprofile + + + + + + + + qaprofile + + + + + + − + LE profile + + + +b+ − − − Hprofile + + + + + + + − Rnprofile − + + +b− − − − Reference Launiainen et al. (2007) Ogée et al. (2003), Porte et al. (2000) Dolman et al. (2002), Moors (2012) Foken et al. (2012), Staudt et al. (2011) Barr et al. (2004) Haverd et al. (2012), Lovell et al. (2012) Knohl et al. (2003) Aubinet et al. (2001), Laitat et al. (1998) aThis site is partially mixed with Pseudotsuga menziesii.bThe LE profile was available for the 2007 and 2008 period, but not 2011, and the Rnprofile was partly available in 2007. 2.3 Model parametrization At the start of this study the multi-layer energy budget did not yet have a working set of parameters for ORCHIDEECAN v1.0. Therefore, we refrained from performing a sensitivity analysis prior to optimizing the model parameters (Kuppel et al., 2014; MacBean et al., 2015), but instead selected three processes described by a total of 10 parameters for optimization. The selected processes were related to the physical processes within the canopy – that is to say, diffusion, advection, and turbulent mixing. 2.3.1 Effective drag coefficient CDeff (unitless) The canopy structure is a very important characteristic for the land–atmosphere interaction, which can now be simulated by the ORCHIDEE-CAN v1.0 land surface model. We assumed that the drag coefficient is scalar independent and can be parametrized by the canopy structure. The effective drag coefficient is used in the one-dimensional second-order closure wind profile model (Massman and Weil, 1999) that was used to estimate the vertical within-canopy wind profile. In this wind profile model (Massman and Weil, 1999), the drag coefficient is assumed to be a constant throughout the canopy layer, but it can also be treated as a function of the vertical canopy structure. In this study, we made use of a prototype parametrization approach proposed by Wohlfahrt and Cernusca (2002). Wohlfahrt and Cernusca (2002) provided the basic idea for considering the effective drag coefficient in grasslands that can be varied due to changes in canopy structure, such as bending effects. Thus, we adapted this parametrization to our model; however, we left the first two tuning coefficients (a1and a2) as constants. This modification allows the effective drag to decrease from a large value to a constant while moving from the top of the canopy to the soil surface layer. Hence, we applied the ideas derived in grassland research to a forest canopy. This approach requires an effective drag coefficient, which relates to the vertically discretized estimate of the canopy drag coefficient (CD,i ; unitless) and the momentum shielding factor (Pm,i; unitless) as follows: CDeff,i =CD,i/Pm,i.(1) Both the within-canopy drag and the momentum shielding were parametrized using a function of the cumulative leaf area index (LAIcum; m2m−2) from the top canopy layer to the bottom layer, which was modified from the original function (Wohlfahrt and Cernusca, 2002) as below: CDeff,i =a−LAIcum,i /a2 1+a−LAIcum,i /a4 3+a5,(2) where the subscript idenotes the index of layering from the bottom layer (i=1) to the top-canopy layer (i=n). a1to a5are tuning coefficients (unitless). The default parameter values for a1to a5are presented in Table 4. www.geosci-model-dev.net/9/2951/2016/ Geosci. Model Dev., 9, 2951–2972, 2016 2956 Y. Chen et al.: Evaluating the performance of ORCHIDEE-CAN v1.0 Table 3. Observation periods for the different data uses in this study. Date format: dd/mm/yy. The information of the energy closure gap for each site over different selected periods was also calculated based on Chen and Li (2012). EXP1: single-layer scheme with a prescribed LAI profile; EXP2: single-layer scheme with a long-term simulated LAI profile; EXP3: multi-layer scheme with a prescribed LAI profile; EXP4: multi-layer scheme with a simulated LAI profile. Site code FI-Hyy FR-LBr NL-Loo DE-Bay CA-Oas AU-Tum DE-Hai BE-Vie Period for short-term parameter optimization (Period I) 01/08/06 14/08/06 31/07/06 05/08/06 08/07/97 12/07/97 04/07/11 17/07/11 16/08/94 22/08/94 08/11/06 11/11/06 10/05/01 19/05/01 01/08/02 07/08/02 Closure gap (W m−2) 43.34 41.56 10.48 18.97 19.82 18.40 29.89 28.19 Period for long-term parameter optimization (Period II) 01/01/02 31/12/02 01/01/03 31/12/03 01/01/02 31/12/02 01/01/97 31/12/97 01/01/05 31/12/05 01/06/01 31/06/02 01/01/05 31/12/05 01/01/97 31/12/97 Closure gap (W m−2) 11.47 21.59 15.38 42.47 2.89 7.12 27.83 42.43 Period for single-year EXP1 and EXP3 validation (Period III) 01/01/05 31/12/05 01/01/06 31/12/06 01/01/97 31/12/97 01/01/99 31/12/99 01/01/04 31/12/04 01/06/04 31/06/05 01/01/01 31/12/01 01/01/02 31/12/02 Closure gap (W m−2) 10.99 13.20 16.61 50.24 4.13 7.73 23.49 42.43 Period for multi-year EXP2 and EXP4 validation (Period IV) 01/01/02 31/12/06 01/01/03 31/12/06 01/01/02 31/12/06 01/01/97 31/12/99 01/01/04 31/12/05 01/06/01 31/06/05 01/01/00 31/12/06 01/01/97 31/12/06 Closure gap (W m−2) 10.68 17.03 22.65 48.14∗3.51 9.40 23.69 33.77 ∗The forest in 1997–1999 was strongly affected by forest decline; in 2011 the forest was again in a good state. Table 4. Description of parameters, code reference, initial values, and tuning ranges used in the multi-layer energy budget model in this work. Parameter Physical parameter Empirical representation of ORCHIDAS Default Tuning range name name value a1Effective surface drag Bending of tree branches a_1 6.410 Use default a2Effective surface drag Bending of tree branches a_2 0.001 Use default a3Effective surface drag Bending of tree branches a_3 0.434 0.1 to 0.8 a4Effective surface drag Bending of tree branches a_4 −0.751 −0.9 to −0.1 a5Effective surface drag Bending of tree branches a_5 0.071 0.05 to 0.1 a6Eddy diffusivity Inner-canopy turbulent mixing k_eddy_slope 5.0 1.0 to 20.0 a7Eddy diffusivity Inner-canopy turbulent mixing k_eddy_ustar 0.3 0.0 to 0.6 a8Surface–atmosphere conductance Inner-canopy turbulent mixing ks_slope 5.0 1.0 to 20.0 a9Surface–atmosphere conductance Under-story phenology ks_veget 0.5 0.0 to 1.0 a10 Surface–atmosphere conductance Under-story phenology ks_tune 1.0 0.5 to 1.5 Wbr Layer boundary resistance Upscaling the leaf coupling br_fac 1.0 0.1 to 10.0 Wsr Layer stomatal resistance Upscaling the leaf coupling sr_fac 1.0 0.1 to 10.0 2.3.2 Eddy diffusivity for vertical energy and water transport k(m2s−1) After the vertical wind profile was derived from the onedimensional second-order closure wind profile model, the friction velocity (u∗, ms−1), the vertical wind velocity variance (σw; ms−1), and the Lagrangian timescale (TL; s) were calculated following the approach by Raupach (1989). In this approach the vertical eddy diffusivity is a function of σwand TL. Subsequently, the vertical eddy diffusivity down the air column to the forest floor was calculated as follows: ki=σ2 w,i TL,i.(3) Here we followed the approach proposed by Haverd et al. (2009) for the Lagrangian timescale calculation. The Lagrangian timescale is thus calculated as TL,i =0.661−e−4.86(z/hc) 1−e−4.86 hc u∗ .(4) A previous effort to validate this model against in situ observations resulted in a bias of the air temperature proGeosci. Model Dev., 9, 2951–2972, 2016 www.geosci-model-dev.net/9/2951/2016/ Y. Chen et al.: Evaluating the performance of ORCHIDEE-CAN v1.0 2957 0.0 0.5 1.0 1.5 0.0 0.2 0.4 0.6 0.8 1.0 0 Weighting factor, Wnf (unitless) Friction velocity (m s−1 ) a6=5 , a7=0.5 a6=10 , a7=0.5 a6=25 , a7=0.5 (a) 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0 Weighting factor, Wsf (unitless) Vegetation cover, 1−fPgap (m2 m−2 ) a8=10 , a9=0.5 , a10 =1 , Tweek ≥Tg a8=10 , a9=0.5 , a10 =1 , Tweek =278.15 (b) Figure 1. Weighting functions for eddy diffusivity and surface conductance. (a) Weighting function for the eddy diffusivity (k) within the air column (Eq. 3). The weighting is a function of the friction velocity (u∗) and was optimized by tuning the parameters a6and a7. Three different parameter sets show the response of the weighting function to different parameter values. (b) The weighting function for the surface conductance is a function of the vegetation cover and air temperature (Eq. 7). This weighting function was optimized by tuning the parameters a8to a10. Two examples have the following parameter values: a8=10.0, a9=0.5, a10 =1.0, Tweek ≥Tg, and Tweek =278.15. Both of the two cases demonstrate the seasonal cycle of the weighting which will be used to scale the value of ksurf. Values to the left of the deflection point show the effect of an increasing/decreasing over-story cover with an increasing/decreasing temperature in spring/autumn. In spring and autumn, under-story growth, and thus its contribution to evapotranspiration, was assumed to be temperature limited. Values right of the deflection point (a9=0.5) show the dependency of the evapotranspiration on the soil surface layer on the over-story canopy cover when air temperature is no longer limiting under-story growth. file within the canopy layer during nighttime (Ryder et al., 2016). These issues have been well documented in the scientific literature (Gao et al., 1989; Dolman and Wallace, 1991; Makar et al., 1999; Wolfe et al., 2011). One possible, although empirical, solution is to apply a different scaling for ki, according to the time of the day. Here we build on a similar approach but, rather than using the time of the day, we used the calculated friction velocity (u∗=u(hc)×(0.32 − 0.264e−15.1ζ(hc)), where ζis the cumulative function of CDeff and hcis the canopy height) to account for the observed differences in vertical transport within the canopy between daytime and nighttime by applying a weighting factor (Wnf; unitless). Therefore the modified diffusivity for level i(k∗ i; m2s−1) was defined as k∗ i=Wnfσ2 w,iTL,i,(5) where Wnf was calculated as Wnf =1 1+e(−a6(u∗−a7)) .(6) This function has a sigmoidal shape, where a6is the ceiling factor of the slope and a7is the critical friction velocity at the inflection point of the sigmoid function (Fig. 1a). Consequently, atmospheric diffusivity is reduced if u∗is low, which represents stable atmospheric conditions. Under turbulent atmospheric conditions, which are represented by a high u∗,Wnf is close to 1 and the simulated diffusivity will closely follow the relationship proposed by Raupach (1991). The default parameter values for a6and a7are presented in Table 4. As an alternative to using u∗, it has been proposed to use a mixing length scale to classify flow regimes in order to give a better description of the coupling process below and above the forest canopy (Thomas and Foken, 2007; Staudt et al., 2011; Foken et al., 2012). The numerical scheme of this approach relies on iterations. Since ORCHIDEE-CAN v1.0 is designed to be coupled to regional or global atmospheric models, its numerics has been designed to avoid iterations in order to run efficiently. Future studies may focus on replacing this empirical solution by a more mechanistic solution. In the context of ORCHIDEE and its coupling to the atmospheric model, this implies that we will have to search for an implicit solution of the near-field far-field theory by Raupach (1989). 2.3.3 Conductance for the soil–atmosphere interface ksurf (ms−1) Equation (7) describes the seasonality of the soil–atmosphere interface, which we believe is driven by the under-story and its phenology (Launiainen et al., 2015). Currently, the model does not simulate the production or the phenology of the under-story. As a substitute for this rather complex process, we made use of a weighting coefficient for the conductance of the soil–atmosphere interface (ksurf) or, in other words, the calculation of the water vapour exchange between the soil www.geosci-model-dev.net/9/2951/2016/ Geosci. Model Dev., 9, 2951–2972, 2016 2958 Y. Chen et al.: Evaluating the performance of ORCHIDEE-CAN v1.0 layer and the first air column (see the φλE and Ksurf in Fig. 1 of Ryder et al., 2016, and the formal description of using Ksurf, which is given in the Supplement of Ryder et al., 2016, in Eqs. S4.30 and S4.31). A relationship between under-story phenology and the conductance for the soil–atmosphere interface has been observed in boreal forest (Launiainen et al., 2015). In winter, when the under-story is senescent, the characteristics in terms of the evapotranspiration at the interface will closely resemble the evapotranspiration of a bare soil. In summer, however, an under-story will be present and its density relates to the gap fraction of the over-story canopy. Hence, the summertime evapotranspiration of the interface will be closer to the evapotranspiration of a vegetation canopy. Therefore, we introduced β0(unitless) as a weighting function ranging from zero to unity, in order to scale the surface conductivity as a function of over-story phenology. Under-story phenology was described as a function of the over-story canopy coverage (1 −fPgap), the mean air temperature during the previous week (Tweek), and a threshold temperature (Tg): β0=              a10 1+e(−a8((1−fPgap)−a9)) , when Gveg =true, a10 1+e(−a8((1−fPgap)−a9)) Tg−Tweek Tg−273.15 , when Gveg =false, (7) where a8is a factor that constrains the slope of the function and a9is a vegetation cover threshold. a10 is a linear weighting factor. Tgis a temperature threshold set to 283.15 K. Gveg is a logic variable to indicate the growth status of the vegetation. Gveg is an existing variable in ORCHIDEE-CAN v1.0 and depends on a threshold for soil water content and temperature Tg. Growth can be expected and therefore Gveg is set to true when the weekly averaged soil water content and temperature exceed the thresholds. fPgap is calculated in ORCHIDEE-CAN v1.0 and describes the over-story gap probability, which is a function of the canopy structure of the vegetation and the solar zenith angle and is calculated in ORCHIDEE-CAN v1.0. For the lowest layer in the air column, i.e. the layer adjacent to the surface, the surface conductance is then calculated as ksurf =(Wsfβ3+(1−Wsf)β4)u1CDeff,1,(8) where β3and β4are coefficients describing respectively the fractions of the potential plant transpiration and soil evaporation that are realized. The definition of these coefficients and the numerical approaches are presented in Ryder et al. (2016) and Dufresne and Ghattas (2009). u1is the wind speed at the lowest canopy layer thus close to the forest floor and is derived from the one-dimensional second-order closure model. CDeff is the effective drag coefficient calculated according to Eq. (2). Wsf is the weighting factor for the soil–atmosphere interface, which is described as the conditional function of over-story canopy cover fraction (1−fPgap). Wsf =β0when (1−fPgap)>a9, and Wsf =1−β0when (1−fPgap ≤a9(see Fig. 1b). The default parameter values of a8,a9,a10, and Wsf are presented in Table 4. 2.3.4 Boundary-layer resistance of the leaf surface Rb (sm−1) The boundary-layer resistance of the leaf surface Rb,i is described according to the expression from Baldocchi (1988): Rb=   Wbr dl Dh,airNu , for sensible heat, Wbr dl Dh,H20Sh , for latent heat,(9) where Wbr accounts for the fact that the leaf length of the species under study differs from the characteristic leaf length (unitless), dlis the characteristic leaf length (0.001 m was used as the default value), Dh,air is the heat diffusivity of still air (m2s−1), Dh,H2Ois the heat diffusivity of water vapour (m2s−1), Sh is the Sherwood number (unitless), and Nu is the Nusselt number (unitless). The Sherwood number was calculated as Sh =0.66Re0.5Sc0.33 for laminar flow and Sh = 0.03Re0.8Sc0.33 for turbulent flow, where Sc is the Schmidt number (0.63 for water vapour; unitless). The transition from laminar to turbulent flow takes place in the model when the Reynolds number exceeds a value of 8000. The Nusselt number was calculated as Nu =0.66RePr0.33, where Pr is the Prandtl number (0.7 for air; unitless) (Grace, 1978) and Re is the Reynolds number (unitless) which was calculated as Re =dlui µ,(10) where uiis the horizontal velocity at level i(ms−1) and µis the kinematic viscosity of air and was set to 0.0015 (m2s−1) (Garratt, 1992). The default parameter value for Wbr is provided in Table 4. 2.3.5 Stomatal resistance Rs(sm−1) The stomatal resistance of the leaves was calculated for each canopy layer based on the parameters within the layer under consideration. Two stomatal resistances were calculated with the concurrent assimilation rate: (a) the stomatal resistance assuming unlimited soil water availability (the atmospheric demand) and (b) the stomatal resistance that exactly satisfies the amount of water the plant can transport from its roots to its stomata (the plant supply). ORCHIDEE-CAN v1.0 calculates the plant supply of the water available for transpiration as the pressure difference between the soil and the leaves divided by the sum of hydraulic resistances of fine roots, sapwood, and leaves (see Eq. 20 in Naudts et al., 2015). The atmospheric demand for water for transpiration is calculated as the vapour pressure difference between the leaves and atmosphere divided by the sum of boundary-layer resistance Geosci. Model Dev., 9, 2951–2972, 2016 www.geosci-model-dev.net/9/2951/2016/ Y. Chen et al.: Evaluating the performance of ORCHIDEE-CAN v1.0 2959 (Rb) and stomatal resistance (Rs) (see Eqs. 9 and 13 in Ryder et al., 2016). When the supply can satisfy the demand, there is no water stress and photosynthesis (A) is calculated. When the demand is limited by the supply term, Aand Rsare recalculated such that they satisfy the supply. Water stress thus enters Eq. (11) in the value of A. ORCHIDEE-CAN v1.0 scales stomatal resistance to account for the part of the canopy that is coupled to the atmosphere and thus contributes to the latent heat flux. In this study, this weighting was formalized through a linear parameter Wsr: Rs,i =Wsr   1 g0+Aihs CsLAIi  ,(11) where g0is the residual stomatal conductance if the solar irradiance approaches zero, Csis the concentration of CO2at the leaf surface, and hsis the relative humidity at the leaf surface. Ais the CO2assimilation rate which is solved analytically following Yin and Struik (2009). In Eq. (11) the relative humidity used is the top-canopy forcing instead of a layered relative humidity in order to avoid an iterative process. The default parameter value for Wsr is presented in Table 4. 2.4 Model optimization Optimization procedure Parametrizing the scaling coefficients and weighting factors enabled us to simultaneously improve the match between the simulated and observed sub-canopy micrometeorology, including temperature and specific humidity when available, and between the simulated and observed top-canopy heat fluxes (LE and H). Within-canopy fluxes were also simulated but are not usually measured. The parametrization made use of an in-house optimization package called ORCHIDAS (ORCHIDEE Data Assimilation Systems; http:// orchidas.lsce.ipsl.fr/). ORCHIDAS provides a range of numerical approaches for assimilating multiple data streams in ORCHIDEE. We used the maximum gradient approach to tune the parameters a3to a10,Wbr, and Wsr for each study site independently. Over the course of several iterations, the optimization approach minimized the mismatch between the model output and the observations, using a gradient-based algorithm called L-BFGS-B (Limited-memory Broyden–Fletcher–Goldfarb– Shanno algorithm with Bound constraints), which provides the possibility to prescribe boundaries for each parameter (Byrd et al., 1995). The range assigned to each parameter is reported in Table 4. Furthermore, this approach allowed for measurement uncertainties in the eddy covariance LE measurement by reducing its weight in the cost function from 1.0 to 0.66. This value of 0.66 was set based on the outcome of a paired tower experiment to estimate the random errors of the eddy covariance measurements (Richardson et al., 2006). For the optimization, the LAI in ORCHIDEE-CAN v1.0 was set to match the observed vertical LAI profile. A three-step optimization procedure was carried out in this study. Firstly, the within-canopy and below-canopy observations from the short-term intensive measurement campaigns (Period I in Table 3) were used to optimize a3to a7,Wbr, and Wsr. During this step, the parameters for the soil–atmosphere interface (ksurf, i.e. a8to a10 and Wsf) were set to their default values. Since these campaigns took place during summer, parameters related to the within-canopy effective drag profiles, eddy diffusivity, boundary layer resistance, and stomatal resistance (CDeff;k;Rb;Rs) were biased towards the summer. Secondly, the seasonal dynamics of ksurf was parametrized by trying to improve the correspondence between the simulated and observed top-canopy fluxes over 1 year (Period II in Table 4). In this step, a3to a7,Wbr, and Wsr were set to the values obtained from the first step of the optimization and a8to a10 and Wsf were tuned. Finally, the performance of the calibrated model was evaluated based on a second single year of top-canopy observations (Period III in Table 3). Although the spin-up was stopped on 30 June (Table S1 in the Supplement) and all simulations thus used the 30 June soil water content as their initial condition, this approach does not guarantee that this typical summer soil water content matches the soil water content in the year of the intensive measurement campaign. The effect of this possible mismatch was quantified by running a sensitivity analysis in which the whole parametrization approach, which was repeated for seven different initial soil water contents, varied from −30 to 30 % in increments of 10 % of the 30 June value. 2.5 Attribution of changes in model performance The multi-layer energy budget scheme (Ryder et al., 2016) that was parametrized and tested in this study required realistic spatial and temporal soil water content and a value for the ground heat flux from surface level as initial conditions. This need was satisfied by implementing this scheme within the newly enhanced ORCHIDEE-CAN v1.0 land surface model (Naudts et al., 2015). Integration of the multi-layer energy budget into ORCHIDEE-CAN v1.0, however, complicated the design of the validation study, as it was now necessary to separate, as much as possible, the performance of the multilayer energy budget scheme from the performance of the rest of the model. To this aim, four experiments were designed in order to better understand the performance of the new scheme (Table S1 in the Supplement). – Experiment 1 (EXP1): single-layer scheme with a prescribed canopy The first experiment was run at the site level and made use of the default single-layer energy budget scheme. The energy budget scheme was driven by the observed climate forcing and the observed total LAI (Table 2). In this experiment, the vertical LAI profile was only www.geosci-model-dev.net/9/2951/2016/ Geosci. Model Dev., 9, 2951–2972, 2016 2966 Y. Chen et al.: Evaluating the performance of ORCHIDEE-CAN v1.0 Rn H LE G One−layer LAI prescribed (EXP1) One−layer LAI simulated (EXP2) Multi−layer LAI prescribed (EXP3) Multi−layer LAI simulated (EXP4) S , Taylor score (0 to 1) T 0.0 0.2 0.4 0.6 0.8 1.0 Figure 7. Change in model performance, expressed as Taylor skill score, with increasing experimental complexity for both the singlelayer and multi-layer energy budget schemes for all eight study sites. EXP1: single-layer scheme with a prescribed LAI profile; EXP2: single-layer scheme with a simulated LAI profile; EXP3: multi-layer scheme with a prescribed LAI profile; EXP4: multilayer scheme with a simulated LAI profile. ulate land surface fluxes dynamically and demonstrated that this approach has difficulties in the reproduction of surface energy fluxes. In this study, we tried to overcome these difficulties by implementing a multi-layer energy budget scheme. The multilayer energy and water calculations make use of a vertically resolved radiation transfer scheme for shortwave and longwave radiation (replacing prescribed shortwave reflection values), a within-canopy wind velocity profile (replacing empirical formulations for roughness length), a vertical prognostic LAI profile (replacing a prescribed LAI value), within-canopy leaf boundary-layer resistance profiles for energy and water transport, a within-canopy stomatal resistance profile, a vertical discrete eddy diffusivity profile, and a soil– atmosphere layer conductivity. This approach resulted in small improvements in simulating energy partitioning during nighttime for dense canopies, small losses in model performance in terms of energy partitioning for sparse canopies, and year-round gains in model performance for simulation of the ground heat flux. As such, the multi-layer energy and water vapour flux scheme did not solve the long-standing issues related to simulating nighttime energy partitioning (Jordan and Smith, 1994; Prihodko et al., 2008; Wild, 2009; He et al., 2011), but it succeeded in obtaining a similar model performance, while much of the empiricism of the big-leaf approach was replaced by a more realistic process description. A more realistic model description opens new avenues of research (see Sect. 4.3). 4.2 Parametrization approach Despite the direction of the land surface model community towards the development of more mechanistic models, all large-scale land surface models contain an important level of empiricism. When the model is carefully developed and validated, the empirical parameters mimic an overly complex (for the purpose of the model) or incompletely understood process. As we tried to follow this philosophy, we believe that our parameters have a plausible natural background (Table 4), but this does not overcome the issue of equifinality of the model. Ideally, future developments should aim at replacing such parameters by a more mechanistic approach if the empirical module represents a process that is at the core of the objectives of the model. In this study, the parametrization of the new scheme and its underlying processes revealed strengths and weaknesses of the model as well as avenues for future experimental work. 1. Within-canopy drag For the inner-canopy drag parametrization, we modified an approach (Eq. 2) that has previously only been tested and validated at grassland sites (Wohlfahrt and Cernusca, 2002). In that study, LAI was treated as equal to the plant area index (PAI), which is a separate measure that accounts not only for leaves, but also for other vegetation material such as stems and seed heads. In forests, however, the difference between LAI and PAI is made up of the branches and trunks and becomes especially important in winter in deciduous stands as canopy drag still exists. As a first parametrization this simplification allowed a better comparison with the observations and with the single-layer model. We applied a formulation that makes use of LAI and, by doing so, some model errors might have been introduced, especially for the deciduous forest sites. ORCHIDEE-CAN v1.0 now simulates both LAI and PAI, and so this enhanced approach could be adopted. Results confirmed that substituting PAI by LAI is acceptable during the leaf-on seasons. Alternative approaches have been proposed by Cescatti and Marcolla (2004). For example, the inner-canopy drag could also be modelled as the function of the percentage of horizontal gaps in the forest canopy – a canopy characteristic that is presently simulated in ORCHIDEE-CAN v1.0. Measurement sites such as DE-Bay or AU-Tum have detailed wind and vertical LAI profile observations and could thus be used in a pilot study for developing a suitable parametrization approach linking inner-canopy drag and shielding to the canopy gaps. Such a development would also meet the requirements for calculating drag and shielding following small-scale mortality from forest management, fires, wind damages, and pests. Geosci. Model Dev., 9, 2951–2972, 2016 www.geosci-model-dev.net/9/2951/2016/ Y. Chen et al.: Evaluating the performance of ORCHIDEE-CAN v1.0 2967 ● ● 0 60 120 180 240 300 0 0.2 0.4 0.6 0.8 1 Latent heat flux (W m−2 ) Normailzed height (z hc −1 ) ●Obs. (13:00) Sim. β3 Sim. β4 Sim. combine (a) ● ● 0 60 120 180 240 300 0 0.2 0.4 0.6 0.8 1 Sensible heat flux (W m−2 ) Normailzed height (z hc −1 ) ●Obs. (13:00) Sim. β3 Sim. β4 Sim. combine (b) Figure 8. Effect of under-story phenology on the vertical profile of the latent and sensible heat fluxes at the FR-LBr site. (a) Simulated latent heat flux assuming that the interface between the soil and the lowest atmospheric layer behaves as a bare soil (dotted line), a fully vegetated surface (dashed line), or a partly vegetated, partly bare surface where the ratio between bare soil and vegetated soil depends on the under-story phenology (full line). The observed profile is shown as black dots where the error bars denote the 5-day temporal variance. (b) Simulated sensible heat flux assuming that the interface between the soil and the lowest atmospheric layer behaves as a bare soil (dotted line) or a fully vegetated surface (dashed line), or depends on the under-story phenology (full line). The observed profile is shown as black dots where the error bars denote the 5-day temporal variance. 2. Within-canopy transport In this study, within-canopy transport was parametrized by K-theory. A one-dimensional second-order closure model was applied to derive the within-canopy turbulence statistics, based both on the LAI profile and the canopy height. This approach has been reported to produce a reasonable approximation of above-canopy fluxes estimation, even if the within-canopy temperature and humidity gradients are not always well captured (Raupach, 1989). As previous studies have demonstrated, incorrect estimation on gradients may be accommodated to some extent by introducing a scaling factor (Eq. 6) to constrain the within-canopy transport (Makar et al., 1999; Wolfe et al., 2011; Ryder et al., 2016). Alternatively, such a scaling factor might vary in terms of the form of the canopy structure or openness, though the determination of the factor has yet to be adequately described due to a restricted range of measurements (McNaughton and Van Den Hurk, 1995; Stroud et al., 2005). At sparse forest sites, the temperature measurements showed a general positive gradient during the daytime (Fig. S5 in the Supplement) and a negative gradient during the nighttime (not shown). For the sparse forests, the temperature gradient is even more complex, having a negative or reversed gradient throughout the vertical profiles. By using the current parametrization approach, most of the sparse forest sites required a higher shear stress (a stronger threshold friction velocity a7) for the within-canopy mixing, compared to dense forest sites (Table S2 in the Supplement), in order to replicate the measurement results. This observation relates to a general difficulty in being able to simulate canopy transport based on limited general measurements (Stroud et al., 2005). 3. Sub-canopy and surface–atmosphere conditions In this study, we treated the under-story and over-story as the same species to construct the vertical LAI profile based on the observed LAI profile. This treatment only allowed the under-story growth to follow overstory canopy phenology. In fact, the forest floor is often occupied by plants with very different traits, of which one of the most obvious is the difference in leaf onset and/or leaf fall (Barr et al., 2004). Given the aforementioned model formulation, simulation of the under-story phenology and traits could be further improved in the future. For example, over-story and under-story vegetation could be simulated as different plant functional types or plant species within the same energy budget www.geosci-model-dev.net/9/2951/2016/ Geosci. Model Dev., 9, 2951–2972, 2016 2968 Y. Chen et al.: Evaluating the performance of ORCHIDEE-CAN v1.0 column. Also, the microclimate created by the overstory could be used as an input to simulate the environmental conditions in the under-story. Starting from the point of view of the interaction between ecosystems and the climate, we introduced a weighting factor (Wsf) as a function of a long-term average temperature and light conditions (gap fraction), a transpiration fractions described as β3in the model code and a soil evaporation fraction (β4) as environmental factors to parametrize surface conductance (Fig. 8 and consequently control the surface latent heat flux. This approach demonstrated the model’s capability to simulate the flux profile in agreement with observations. It may, however, not be valid for the savanna ecosystem because the under-story phenology of this ecosystem relies on water availability in the top soil layer (Baldocchi and Wilson, 2001; Hutley et al., 2000), which is an environmental condition not accounted for in our approach. Furthermore, accounting for ecosystem-specific differences in root density profiles and aerial cover of the under-story might also help in the simulation of water and energy fluxes (El Masri et al., 2015; Launiainen et al., 2015). From this perspective, detailed soil moisture profile observations would be very useful in developing a more advanced surface–atmosphere interface parametrization. 4. Mismatch between low-resolution driver data and vertically resolved vegetation layers In this study an apparent mismatch was present between the low resolution of the driver data that contain information derived from several different land cover types and the highly resolved vertical layering of the canopy. When low-resolution driver data are used, the benefit from replacing the big-leaf approach in favour of a multi-layer approach becomes questionable. In this study the spin-up of the soil water content made use of low-resolution driver data, but the simulations themselves were driven by spatially and temporally high-resolution site observations. Nevertheless, the apparent mismatch touches upon an interesting issue: how does one account for the average surface fluxes from the contribution of different subgrid-scale land cover types? The present ORCHIDEE single-layer model calculates a weighted average of different PFTs across a grid square to calculate a total representative flux. An alternative approach, and one that we are investigating using this multi-layer model in ORCHIDEE-CAN v1.0, is to calculate the heat fluxes of each vegetation type separately (sub-grid-scale modelling) so that the mixing occurs above the canopy. 5. The proposed parametrization approach and future work In general, we provide a simple but useful parametrization approach for the multi-layer energy budget scheme in the ORCHIDEE-CAN v1.0 global land surface model. Comparing with other studies (Ogée et al., 2003; Staudt et al., 2011; Launiainen et al., 2015), our approach directly determines the energy and water fluxes and successfully avoids the iterative processes to meet the numerical requirement. In total, a set of 12 parameters need to be prescribed and calibrated regarding the empirical representation of surface drag, turbulent mixing, sub-canopy phenology, and leaf–atmosphere coupling processes. Our approach presents a good performance at all study sites, though we may have some deficits in wind speed estimation. In this study the model had been tested for several environmental conditions and demonstrated that the numerics can deal with the variation that can be found in global ecosystems. A separate parameter set for each site has been provided. Next, we will have to derive a single parameter set for each PFT and test how well the model reproduces global patterns in, for example, evapotranspiration. Only then will we be able to learn about the transferability of the parameters from the site level to the PFT level. 4.3 Increased model capacity The innovation of the multi-layer energy and water scheme is the capacity to simulate the behaviour of fluxes within the canopy and the separation of the soil-level temperature from the temperature of the vegetation levels. The multilayer scheme helps to address how forest management such as thinning or shelterwood cutting may alter the forest– atmosphere coupling and resulting fluxes. It also paves the way for the consideration of mixed forests where different plant species or functional types can be in a different microclimatic environment to that of the high canopy. This capacity is essential for the following types of future potential applications. 1. The simulation of emission of biogenic volatile organic compounds (BVOCs) from plants, linking climate change, atmospheric chemistry, and the terrestrial biosphere. The implemented multi-layer energy and water budget calculates the leaf temperature and withincanopy radiation, and therefore would potentially allow us to simulate the emission of BVOCs, such as isoprene or monoterpene from plants (Guenther et al., 1995, 2006). 2. Natural disturbances, such as fires, pests, and windfall, can result in increases in leaf fall, individual tree mortality, or complete stand destruction (Lugo, 2008; Seidl et al., 2011; Yue et al., 2014), which in turn determine the vertical LAI profile. The implemented multi-layer energy and water budget scheme calculates the vertical eddy diffusivity and effective drag coefficient as a function of the vertical LAI profile; hence, the new scheme Geosci. Model Dev., 9, 2951–2972, 2016 www.geosci-model-dev.net/9/2951/2016/ Y. Chen et al.: Evaluating the performance of ORCHIDEE-CAN v1.0 2969 allows the study of effects of changes in disturbance intensity on the energy budget and thus the climate system. 3. Forest canopy structure plays an important role in regulating the provision of forest ecosystem services such as maintaining biodiversity (Scheffers et al., 2013; Defraeye et al., 2014) or regulating streamflow (Jackson, 2005). Therefore, structural changes to the forest canopy, through, for example, forest thinning or species changes, will reduce the buffering effect of the canopy. It is only with models including a multi-layer energy budget that an informed prediction of the long-term consequences of land-management policies can be made. 4. This work takes the first step in exploring the use of vertical canopy profiles in coupled vegetation/atmospheric models, particularly in relation to the calculation of GPP, which is sensitive to the vertical profiles of light, water and nitrogen (Bonan et al., 2012, 2014). To run at a regional or global scale, it is essential to first parametrize the model at the site level. 5 Conclusion Although the first parametrization of a multi-layer energy and water budget scheme did not greatly improve the model performance over the use of the so-called big-leaf approach for energy and water calculations, it provides a more detailed description of the within-canopy micrometeorology of various forest types. A more detailed process description is essential when linking climate change to studies addressing, for example, species vulnerability to climate change, the climate feedbacks from different disturbance intensities, changes in under-story habitat following management changes, and BVOCs as a result of climate change. In this study, multiple-site calibration and optimization were performed in order to better understand the functionality of the newly implemented multi-layer energy budget in ORCHIDEE-CAN v1.0. Developing the multi-layer energy budget requires accurate field measurements for model calibration and validation. Here we were able to collect and make use of many of the few data sets that exist for intensive in-canopy profile time series measurements. We suggest that more intensive field campaigns, with soil water content observations, especially during the winter season, would help in the development of a more reliable parametrization scheme for the within-canopy eddy diffusivity and soil– atmosphere interface conductance. For future model developments, adding an extra soil–atmosphere interface representation such as moss or herbs on the forest floor would be beneficial for a more complete multi-layer energy budget with the objective of describing the surface–atmosphere interface gas and water vapour exchanges. 6 Code and data availability The code and the run environment are open source. Nevertheless, readers interested in running ORCHIDEE-CAN v1.0 (revision 2754) are encouraged to contact the corresponding author for full details and the latest bug fixes. The ORCHIDEE-CAN branch is available via the follow web link: https://forge.ipsl.jussieu.fr/orchidee/browser/branches/ ORCHIDEE-DOFOCO/ORCHIDEE. The Supplement related to this article is available online at doi:10.5194/gmd-9-2951-2016-supplement. Author contributions. Yiying Chen, James Ryder, and Sebastiaan Luyssaert developed the parametrization scheme. Yiying Chen, Sebastiaan Luyssaert, and Philippe Peylin designed the study and Yiying Chen wrote the manuscript with contributions from all co-authors. James Ryder, Matthew J. McGrath, Juliane Otto, Kim Naudts, Sebastiaan Luyssaert, and Aude Valade helped Yiying Chen with integrating the parametrization scheme for the multilayer energy budget in ORCHIDEE-CAN v1.0. Vladislav Bastrikov and Philippe Peylin provided the optimization tools and helped with the configuration of these tools. Eva van Gorsel, Vanessa Haverd, Bernard Heinesch, Alexander Knohl, Samuli Launiainen, Denis Loustau, Eddy Moors, Jérôme Ogée, Thomas Foken, and Timo Vessala provided field observations for all study sites. Acknowledgements. Yiying Chen, James Ryder, Matthew J. McGrath, Juliane Otto, Kim Naudts and Sebastiaan Luyssaert were funded through ERC starting grant 242564 (DOFOCO), and Aude Valade was funded through ADEME (BiCaFF). We thank the two anonymous reviewers for their very helpful observations and suggestions. Edited by: J. Kala Reviewed by: two anonymous referees References Aubinet, M., Chermanne, B., Vandenhaute, M., Longdoz, B., Yernaux, M., and Laitat, E.: Long term carbon dioxide exchange above a mixed forest in the Belgian Ardennes, Agr. Forest Meteorol., 108, 293–315, doi:10.1016/S0168-1923(01)00244-1, 2001. Baldocchi, D.: A Multi-layer model for estimating sulfur dioxide deposition to a deciduous oak forest canopy, Atmos. Environ., 22, 869–884, doi:10.1016/0004-6981(88)90264-8, 1988. Baldocchi, D. D. and Wilson, K. B.: Modeling CO2and water vapor exchange of a temperate broadleaved forest across hourly to decadal time scales, Ecol. Model., 142, 155–184, doi:10.1016/S0304-3800(01)00287-3, 2001. Barr, A. G., Black, T. A., Hogg, E. 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